






"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE   :							Tuple          =		logging.get_logger(__name__)  # pylint: disable=invalid-name






class 	a		(   lowercase__						):






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				AutoencoderKL			,		__SCREAMING_SNAKE_CASE	:				CLIPTextModel			,		__SCREAMING_SNAKE_CASE	:				CLIPTokenizer			,		__SCREAMING_SNAKE_CASE	:				UNetaDConditionModel			,		__SCREAMING_SNAKE_CASE	:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,		__SCREAMING_SNAKE_CASE	:				StableDiffusionSafetyChecker			,		__SCREAMING_SNAKE_CASE	:				CLIPImageProcessor			,		)	->						str:
      super().__init__()
      self.register_modules(
          vae=SCREAMING_SNAKE_CASE_			,		text_encoder=SCREAMING_SNAKE_CASE_			,		tokenizer=SCREAMING_SNAKE_CASE_			,		unet=SCREAMING_SNAKE_CASE_			,		scheduler=SCREAMING_SNAKE_CASE_			,		safety_checker=SCREAMING_SNAKE_CASE_			,		feature_extractor=SCREAMING_SNAKE_CASE_			,		)






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, int]] = "auto"   )	->						Dict:


      if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowerCamelCase_       	=		self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_   )






    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      self.enable_attention_slicing(SCREAMING_SNAKE_CASE_   )






    @torch.no_grad()
    def __call__(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				Union[str, List[str]]			,		__SCREAMING_SNAKE_CASE	:				int = 512			,		__SCREAMING_SNAKE_CASE	:				int = 512			,		__SCREAMING_SNAKE_CASE	:				int = 50			,		__SCREAMING_SNAKE_CASE	:				float = 7.5			,		__SCREAMING_SNAKE_CASE	:				Optional[Union[str, List[str]]] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[int] = 1			,		__SCREAMING_SNAKE_CASE	:				float = 0.0			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.Generator] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.FloatTensor] = None			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = "pil"			,		__SCREAMING_SNAKE_CASE	:				bool = True			,		__SCREAMING_SNAKE_CASE	:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,		__SCREAMING_SNAKE_CASE	:				int = 1			,		__SCREAMING_SNAKE_CASE	:				Optional[torch.FloatTensor] = None			,		**__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		)	->						int:


      if isinstance(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
        lowerCamelCase_       	=		1
      elif isinstance(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
        lowerCamelCase_       	=		len(SCREAMING_SNAKE_CASE_   )
      else:
        raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_   )}'''   )

      if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ) or callback_steps <= 0)
      ):
        raise ValueError(
            F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
            F''' {type(SCREAMING_SNAKE_CASE_   )}.'''   )

      # get prompt text embeddings
      lowerCamelCase_       	=		self.tokenizer(
          SCREAMING_SNAKE_CASE_			,		padding='max_length'			,		max_length=self.tokenizer.model_max_length			,		return_tensors='pt'			,		)
      lowerCamelCase_       	=		text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        lowerCamelCase_       	=		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
        logger.warning(
            'The following part of your input was truncated because CLIP can only handle sequences up to'
            F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''   )
        lowerCamelCase_       	=		text_input_ids[:, : self.tokenizer.model_max_length]

      if text_embeddings is None:
        lowerCamelCase_       	=		self.text_encoder(text_input_ids.to(self.device   )   )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		text_embeddings.shape
      lowerCamelCase_       	=		text_embeddings.repeat(1			,		SCREAMING_SNAKE_CASE_			,		1   )
      lowerCamelCase_       	=		text_embeddings.view(bs_embed * num_images_per_prompt			,		SCREAMING_SNAKE_CASE_			,		-1   )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      lowerCamelCase_       	=		guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
        lowerCamelCase_       	=		42
        if negative_prompt is None:
          lowerCamelCase_       	=		['']
        elif type(SCREAMING_SNAKE_CASE_   ) is not type(SCREAMING_SNAKE_CASE_   ):
          raise TypeError(
              F'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_   )} !='''
              F''' {type(SCREAMING_SNAKE_CASE_   )}.'''   )
        elif isinstance(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   ):
          lowerCamelCase_       	=		[negative_prompt]
        elif batch_size != len(SCREAMING_SNAKE_CASE_   ):
          raise ValueError(
              F'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_   )}, but `prompt`:'''
              F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
              ' the batch size of `prompt`.'   )
        else:
          lowerCamelCase_       	=		negative_prompt

        lowerCamelCase_       	=		text_input_ids.shape[-1]
        lowerCamelCase_       	=		self.tokenizer(
            SCREAMING_SNAKE_CASE_			,		padding='max_length'			,		max_length=SCREAMING_SNAKE_CASE_			,		truncation=SCREAMING_SNAKE_CASE_			,		return_tensors='pt'			,		)
        lowerCamelCase_       	=		self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        lowerCamelCase_       	=		uncond_embeddings.shape[1]
        lowerCamelCase_       	=		uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		1   )
        lowerCamelCase_       	=		uncond_embeddings.view(batch_size * num_images_per_prompt			,		SCREAMING_SNAKE_CASE_			,		-1   )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowerCamelCase_       	=		torch.cat([uncond_embeddings, text_embeddings]   )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      lowerCamelCase_       	=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      lowerCamelCase_       	=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
      lowerCamelCase_       	=		text_embeddings.dtype
      if latents is None:
        if self.device.type == "mps":
          # randn does not exist on mps
          lowerCamelCase_       	=		torch.randn(
              SCREAMING_SNAKE_CASE_			,		generator=SCREAMING_SNAKE_CASE_			,		device='cpu'			,		dtype=SCREAMING_SNAKE_CASE_   ).to(self.device   )
          lowerCamelCase_       	=		torch.randn(SCREAMING_SNAKE_CASE_			,		generator=SCREAMING_SNAKE_CASE_			,		device='cpu'			,		dtype=SCREAMING_SNAKE_CASE_   ).to(
              self.device   )
        else:
          lowerCamelCase_       	=		torch.randn(
              SCREAMING_SNAKE_CASE_			,		generator=SCREAMING_SNAKE_CASE_			,		device=self.device			,		dtype=SCREAMING_SNAKE_CASE_   )
          lowerCamelCase_       	=		torch.randn(SCREAMING_SNAKE_CASE_			,		generator=SCREAMING_SNAKE_CASE_			,		device=self.device			,		dtype=SCREAMING_SNAKE_CASE_   )
      else:
        if latents_reference.shape != latents_shape:
          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
        lowerCamelCase_       	=		latents_reference.to(self.device   )
        lowerCamelCase_       	=		latents.to(self.device   )

      # This is the key part of the pipeline where we
      # try to ensure that the generated images w/ the same seed
      # but different sizes actually result in similar images
      lowerCamelCase_       	=		(latents_shape[3] - latents_shape_reference[3]) // 2
      lowerCamelCase_       	=		(latents_shape[2] - latents_shape_reference[2]) // 2
      lowerCamelCase_       	=		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
      lowerCamelCase_       	=		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
      lowerCamelCase_       	=		0 if dx < 0 else dx
      lowerCamelCase_       	=		0 if dy < 0 else dy
      lowerCamelCase_       	=		max(-dx			,		0   )
      lowerCamelCase_       	=		max(-dy			,		0   )
      # import pdb
      # pdb.set_trace()
      lowerCamelCase_       	=		latents_reference[:, :, dy : dy + h, dx : dx + w]

      # set timesteps
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      lowerCamelCase_       	=		self.scheduler.timesteps.to(self.device   )

      # scale the initial noise by the standard deviation required by the scheduler
      lowerCamelCase_       	=		latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      lowerCamelCase_       	=		'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      lowerCamelCase_       	=		{}
      if accepts_eta:
        lowerCamelCase_       	=		eta

      for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_   )   ):
        # expand the latents if we are doing classifier free guidance
        lowerCamelCase_       	=		torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
        lowerCamelCase_       	=		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )

        # predict the noise residual
        lowerCamelCase_       	=		self.unet(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		encoder_hidden_states=SCREAMING_SNAKE_CASE_   ).sample

        # perform guidance
        if do_classifier_free_guidance:
          lowerCamelCase_	,      lowerCamelCase_       	=		noise_pred.chunk(2   )
          lowerCamelCase_       	=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        lowerCamelCase_       	=		self.scheduler.step(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		**SCREAMING_SNAKE_CASE_   ).prev_sample

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
          callback(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_   )

      lowerCamelCase_       	=		1 / 0.18_215 * latents
      lowerCamelCase_       	=		self.vae.decode(SCREAMING_SNAKE_CASE_   ).sample

      lowerCamelCase_       	=		(image / 2 + 0.5).clamp(0			,		1   )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   ).float().numpy()

      if self.safety_checker is not None:
        lowerCamelCase_       	=		self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_   )			,		return_tensors='pt'   ).to(
            self.device   )
        lowerCamelCase_	,      lowerCamelCase_       	=		self.safety_checker(
            images=SCREAMING_SNAKE_CASE_			,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype   )   )
      else:
        lowerCamelCase_       	=		None

      if output_type == "pil":
        lowerCamelCase_       	=		self.numpy_to_pil(SCREAMING_SNAKE_CASE_   )

      if not return_dict:
        return (image, has_nsfw_concept)

      return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_			,		nsfw_content_detected=SCREAMING_SNAKE_CASE_   )






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class 			lowercase					( lowercase__   ,lowercase__		):
			lowercase        =     '''nat'''

			lowercase        =     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=4      ,SCREAMING_SNAKE_CASE_  :	Dict=3      ,SCREAMING_SNAKE_CASE_  :	int=64      ,SCREAMING_SNAKE_CASE_  :	Tuple=[3, 4, 6, 5]      ,SCREAMING_SNAKE_CASE_  :	Dict=[2, 4, 8, 16]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=7      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3.0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Tuple="gelu"      ,SCREAMING_SNAKE_CASE_  :	str=0.02      ,SCREAMING_SNAKE_CASE_  :	str=1e-5      ,SCREAMING_SNAKE_CASE_  :	int=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,)       ->	Tuple:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  patch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  embed_dim
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  num_heads
				lowerCAmelCase												=  kernel_size
				lowerCAmelCase												=  mlp_ratio
				lowerCAmelCase												=  qkv_bias
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  initializer_range
				# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				lowerCAmelCase												=  int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_			) - 1)			)
				lowerCAmelCase												=  layer_scale_init_value
				lowerCAmelCase												=  ['''stem'''] + [F"""stage{idx}""" for idx in range(1      ,len(SCREAMING_SNAKE_CASE_			) + 1			)]
				lowerCAmelCase							,       lowerCAmelCase												=  get_aligned_output_features_output_indices(
				    out_features=SCREAMING_SNAKE_CASE_      ,out_indices=SCREAMING_SNAKE_CASE_      ,stage_names=self.stage_names			)


